Assessing differentially private deep learning with Membership Inference

Abstract

Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set. A possible protection is offered by anonymization of the training data or training function with differential privacy. However, data scientists can choose between local and central differential privacy and need to select meaningful privacy parameters $\epsilon$ which is challenging for non-privacy experts. We empirically compare local and central differential privacy mechanisms under white- and black-box membership inference to evaluate their relative privacy-accuracy trade-offs. We experiment with several datasets and show that this trade-off is similar for both types of mechanisms. This suggests that local differential privacy is a sound alternative to central differential privacy for differentially private deep learning, since small $\epsilon$ in central differential privacy and large $\epsilon$ in local differential privacy result in similar membership inference attack risk.

References in corpus (5)

• Deep Learning with Differential Privacy
• Renyi Differential Privacy
• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
• Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
• An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

Cited by in corpus (6)

• Privacy-Preserving Machine Learning: Methods, Challenges and Directions
• Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
• Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
• Large Scale Private Learning via Low-rank Reparametrization
• Hide-and-Seek Privacy Challenge
• Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?